Cell-free prototyping enables implementation of optimized reverse β-oxidation pathways in heterotrophic and autotrophic bacteria

Abstract

Carbon-negative synthesis of biochemical products has the potential to mitigate global CO2 emissions. An attractive route to do this is the reverse β-oxidation (r-BOX) pathway coupled to the Wood-Ljungdahl pathway. Here, we optimized and implemented r-BOX for the synthesis of C4-C6 acids and alcohols. With a high-throughput in vitro prototyping workflow, we screened 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity. Implementation of these pathways into Escherichia coli generated designer strains for the selective production of butanoic acid (4.9 +/- 0.1 gL-1), hexanoic acid (3.06 +/- 0.03 gL-1) and 1-hexanol (1.0 +/- 0.1 gL-1) at the best performance reported to date in this bacterium. We also generated Clostridium autoethanogenum strains able to produce 1-hexanol from syngas, achieving a titer of 0.26 gL-1 in a 1.5-L continuous fermentation. Our strategy enables optimization of rBOX derived products for biomanufacturing and industrial biotechnology.

A metagenome-level analysis of a microbial community fermenting ultra-filtered milk permeate

“…Our inferences regarding LCO1 and ACID1 producing longer chain fatty acids were made using similar criteria. Isolated organisms have shown a preference for chain elongation products of different lengths, (e.g., C. tyrobutyricum produces butyric acid while CPB6 produces butyric and hexanoic acids) (Wu and Yang, 2003;Zhu et al, 2017) and recent studies utilizing cell-free prototyping to screen enzyme homologs have indicated that thioloases (ACAT) and terminal enzymes of reverse β-oxidation are key determinants in chain length selection (Tarasava et al, 2022;Vögeli et al, 2022). These findings warrant further investigation regarding their implementation in elucidating genomic features for the determination of chain length specificity, possibly through a phylogenetic comparison of enzyme homologs.…”

Metabolic perturbations to anE. coli-based cell-free system reveal a trade-off between transcription and translation

“…In recent years, cell-free systems have become an important tool for protein engineering, structural biology, and drug discovery, enabling production of functional proteins such as enzymes, antibodies, and vaccine candidates [5,11]. More broadly, cell-free systems have been used for applications such as rapid and high-throughput prototyping of engineered biological components and systems [16,18,31,30,35], biosensing and diagnostics [26,38,22], and the development of synthetic life [8,37].…”

Dynamic Kinetic Models Capture Cell-Free Metabolism for Improved Butanol Production

“…This challenge is due to several factors, including the competition in living cells between non-native biosynthesis and cell viability (Dudley et al, 2015; Rasor et al, 2021; Wu et al, 2016), the highly connected nature of metabolic networks which obscures simple design choices for optimization (Tsiantis & Banga, 2020), and the difficulty of obtaining high-quality, kinetic data from living cells (Costa et al, 2016; Karim & Jewett, 2016). Cell-free systems addresses some of these issues, as cell viability no longer interferes with pathway flux, and time-course metabolomics are readily available without the need to extract intracellular species (Horvath et al, 2020; Karim et al, 2020; Miguez et al, 2019, 2021; Silverman et al, 2020; Vogeli et al, 2022). However, unlike purified systems, cell extract-based cell-free systems maintain much of the complexity of living metabolic networks (Bowie et al, 2020), which allows for native energy and cofactor regeneration to fuel biosynthesis (Jewett et al, 2008) but also preserves the complexity arising from network connectivity, making optimization nontrivial.…”